紡織品的顏色因為不同凹凸編織方式會造成顏色變異性大，使得色彩檢測難度增加。為了提高紡織品色彩識別的準確度，本研究提出結合自組織映射(SOM)神經網路的色彩檢測方法。針對一個具有複雜紋理的彩色紡織品，我們將影像透過色彩空間轉換將RGB色彩空間轉換為CIE Lab色彩空間，以SOM神經網路進行色彩分群，計算該影像的色彩分佈直方圖，再根據直方圖取出紡織物主色，最後使用CIE 2000比對色彩偏差的測量，據此進行色彩識別。實驗分別採用ECCV 2016紡織品資料庫樣本以及真實拍攝10種不同顏色的紡織品，我們在每塊紡織品上隨機取得20個區塊影像，共200個樣本來驗證我們的色彩識別方法。結果顯示，在ECCV 2016紡織品資料庫，使用我們的色彩檢測方法可以將原本EER=8%的辨識錯誤率降低至EER=3.5%；在真實環境所拍攝的紡織品辨識實驗，我們的色彩檢測方法在SOM分群數為16類的情況下，可以將EER=13%的辨識錯誤率降低至EER=1.0%。

Different uneven weaving methods would cause a large color variability of textiles, increasing the degree of difficulty in detecting colors. To increase the accuracy in color recognition of textiles, we proposed a method combining a self-organizing map (SOM) neural network for color detection in this study. In this method, RGB images were converted into CIE Lab counterparts through color space for colored textiles with complex textures, where the SOM neural network was used to perform color clustering, and to calculate the image color histogram (distribution). Further, main colors of the textiles were extracted from the histogram, and finally the CIE 2000 formula was used to compare the deviation of color measurements, based on which the color recognition was performed. Experimentally, samples of the textile database of ECCV 2016 and filmed textile images with ten colors were employed, where twenty blocks of images were randomly sampled from each textile, for a total of 200 samples used to verify the color recognition method. The results indicated that, for the samples of the textile database of ECCV 2016, using the color detection method proposed in this study could reduce the recognition error rate from 8% (EER=8%) to 3.5% (EER=3.5%), while, for the filmed textile images, using our SOM color detection method could reduce the recognition error rate from 13% (EER=13%) to 1.0% (EER=1.0%) for a number of clusters of 16.

[1] Roland Szabo and Ioan Lie, “Automated colored object sorting application for robotic arms”, Electronics and Telecommunications(ISETC), pp. 95 - 98, 2012.
[2] Jessie Darma, Frederick, Leonardy Setyawan, and Endrowednes Kuantama, “Color recognition robot”, Communication Software and Networks(ICCSN), pp. 85 - 89, 2011.
[3] Chandra Sekhar Nandi, Bipan Tudu, and Chiranjib Koley, “An automated machine vision based system for fruit sorting and grading”, Sensing Technology(ICST), pp. 195 - 200, 2015.
[4] Roland Szabo and Aurel Gontean, and Alexandru Sfirat, “Robotic arm control in space with color recognition using a Raspberry PI”, Telecommunications and Signal Processing(TSP), pp. 689 - 692, 2016.
[5] B. Jeeva, J. R. Suresh Babu, and M. Chitra, “Study of inter-integrated circuit color sensor with microcontroller”, Emerging Trends in Electrical Engineering and Energy Management(ICETEEEM), pp. 1 - 5, 2012.
[6] Stefano Ghidoni, Matteo Finotto, and Emanuele Menegatti, “Automatic Color Inspection for Colored Wires in Electric Cables”, IEEE Transactions on Automation Science and Engineering, vol.12, no.5, pp. 569 - 607, 2015.
[7] J. C. Gonzalez Islas, D. de J. Cano Tejeda, J. Suarez Cansino, and V. Lopez Morales, ”Intelligent system for inspection and selection of parts in a manufacturing cell”, Information, Communication and Automation Technologies(ICAT), pp. 1 - 6, 2013.
[8] Chun-Ling Yang and Bing Wang, “Fuzzy algorithm for color recognition and realization on FPGA”, Industrial Electronics and Applications(ICEAT), pp. 1529 - 1533, 2015.
[9] My-Ha Le, Van-Dung Hoang, and Van-Thuyen Ngo, “Electrical colored wires inspection algorithm for automatic connector producing machines”, System Science and Engineering(ICSSE), pp. 1 - 4, 2016.
[10] T. L. Nguyen, R. Boukezzoula, D. Coquin, E. Benoit, and S. Perrin, “Interaction between humans, NAO robot and multiple cameras for colored objects recognition using information fusion”, Human System Interaction(HSI), pp. 322 - 328, 2015.
[11] Wang Chunhong, Zhang Hongqiang, and Yu Changxing, “Research on Color Recognition of Urine Test Paper Based on Learning Vector Quantization (LVQ)”, Instrumentation, Measurement, Computer, Communication and Control(IMCCC), pp. 850 - 853, 2012.
[12] Cui Liu and Lianming Wang, “Multi-scale fuzzy color recognition and segmentation of color image”, Natural Computation, Natural Computation, Fuzzy Systems and Knowledge Discovery(ICNC-FSKD), pp. 984 - 988, 2016.
[13] A. H. Fauzi, D. N. F. Awang Iskandar and M. Alif A. Suhaimi, “Automated machine for sorting Sarawak pepper berries”, IT in Asia(CITA), pp. 1 - 4, 2015.
[14] Mahdi Pedram, Seyed Ali Rokni, Ramin Fallahzadeh, and Hassan Ghasemzadeh, “Poster Abstract: A Beverage Intake Tracking System Based on Machine Learning Algorithms, and Ultrasonic and Color Sensors”, Information Processing in Sensor Networks (IPSN), pp. 313 - 314, 2017.
[15] Sensorpart, “Vision colour sensors for optimum object detection”, [Online] Available: http://www.sensopart.com/en.
[16] SINSICK, “Color Sensor”, [Online] Available: https://www.sick.com/.
[17] OPTEX, “Color Sensors”,[Online] Available: http://www.optex-ramco.com/index.php.
[18] Cheng-Hung Wang, Ying-Chin Lo, and Chia-Yi Huang, “Exploration of video art and color vision: The application of image change in digital interaction”, Advanced Materials for Science and Engineering(ICAMSE), pp. 512 - 515, 2016.
[19] M. Moghavvemi, S. S. Jamuar, E. H. Gan, and Y. C. Yap, “Design of low cost flexible RGB color sensor”, Informatics, Electronics & Vision(ICIEV), pp. 1158 - 1162, 2012.
[20] Joost van de Weijer, Cordelia Schmid, Jakob Verbeek, and Diane Larlus, “Learning Color Names for Real-World Applications”, IEEE Transactions on Image Processing, vol.18, no.7, pp. 1512 - 1523, 2009.
[21] David Hanwell, Majid Mirmehdi, “Weakly supervised learning of semantic colour terms”, IET Computer Vision, vol.8, no.2, pp. 110 - 117, 2014.
[22] Hayriye Altural, N. Ebru Korkmaz, Omer Galip Saracoglu, and Mustafa Tutak, “Adaptation of optical RGB sensor to CIE-XYZ color space”, Signal Processing and Communications Applications(SIU), pp. 1145 - 1148, 2011.
[23] Cui Liu and Lianming Wang, “Fuzzy color recognition and segmentation of robot vision scene”, Image and Signal Processing(CISP), pp. 448 - 452, 2015.
[24] G. Saravanan, G. Yamuna, and S. Nandhini, “Real time implementation of RGB to HSV/HSI/HSL and its reverse color space models”, Communication and Signal Processing(ICCSP), pp. 0462 - 0466, 2016.
[25] Kazuo Misue and Hatsune Kitajima, “Design Tool of Color Schemes on the CIELAB Space”, Information Visualisation(IV), pp. 33 - 38, 2016.
[26] Ganesan, B. S. Sathish, Khamar Basha Shaik, and V. Kalist, “Neural network based SOM for multispectral image segmentation in RGB and HSV color space”, Circuits, Power and Computing Technologies(ICCPCT), pp. 1 - 6, 2015.
[27] Parag Puranik, Preeti Bajaj, Ajith Abraham, Prasanna Palsodkar, and Amol Deshmukh, “Human Perception-Based Color Image Segmentation Using Comprehensive Learning Particle Swarm Optimization”, Emerging Trends in Engineering and Technology(ICETET), pp. 630 - 635, 2009.
[28] Samira Chebbout and Hayet Farida Merouani, “Comparative Study of Clustering Based Colour Image Segmentation Techniques”, Signal Image Technology and Internet Based Systems(SITIS), pp. 839 - 844, 2012.
[29] F. Ebner and M. D. Fairchild, “Development and testing of a color Space (IPT) with improved hue uniformity”, Society for Imaging Science and Technology(IS&T), pp.8 - 13, 1998.
[30] H. B. Kekre and Kavita Sonawane, “Comparative study of color histogram based bins approach in RGB, XYZ, Kekre's LXY and L’X’Y’color spaces”, Circuits, Systems, Communication and Information Technology Applications(CSCITA), pp. 364 - 369, 2014.
[31] Zhen Zhou, Dixing Li, Deyang Lin, and Li Liang, Lifeng Liu, “Application of Multi-color Space Feature Fusion in Color Difference Processing” Instrumentation and Measurement, Computer, Communication and Control(IMCCC), pp. 751 - 755, 2014.
[32] Romi Fadillah Rahmat, Tengku Chairunnisa, Dani Gunawan, and Opim Salim Sitompul, “Skin color segmentation using multi-color space threshold”, Computer and Information Sciences(ICCOINS), pp. 391 - 396, 2016.
[33] Alireza Rahimzadeganasl and Elif Sertel, “Automatic building detection based on CIE LUV color space using very high resolution pleiades images”, Signal Processing and Communications Applications Conference(SIU), pp. 1 - 4, 2017.
[34] F. Bensaali and A. Amira, “Design and Implementation of Efficient Architectures for Color Space Conversion”, Graphics,Vision and Image Processing(ICGST), vol.5, no.1, pp. 37 - 47, 2004.
[35] W. Burger and M. Burge, “Principles of digital image processing : core algorithms”, Springer, ISBN. 978-1848001947, 2009.
[36] Enrif Madina, Ahmed Drissi El Maliani, Mohammed El Hassouni, and Said Ouatik Alaoui, “Study of magnitude and extended relative phase information for color texture retrieval in L*a*b* color space”, Wireless Networks and Mobile Communications(WINCOM), pp. 127 - 132, 2016.
[37] Xi Wang, Ronny Hansch, Lizhuang Ma, and Olaf Hellwich, “Comparison of different color spaces for image segmentation using graph-cut”, Computer Vision Theory and Application(VISAPP), vol.1, pp. 301 - 308, 2014.
[38] D. F. Specht, “Probabilistic neural betworks and polynomial adaline as complementary techniques for classification”, IEEE Transactions on Neural Networks, vol. 1, no.1, pp. 111 - 121, 1990.
[39] Peter Sarlin and Samuel Ronnqvist, “Cluster Coloring of the Self-Organizing Map: An Information Visualization Perspective”, Information Visualisation(IV), pp. 532 - 538, 2013.
[40] Cyril De Runz, Eric Desjardin, and Michel Herbin, “Unsupervised Visual Data Mining Using Self-organizing Maps and a Data-driven Color Mapping”, Information Visualisation(IV), pp. 241 - 245, 2012.
[41] Xiao-Yu Zhang, Jiu-Sheng Chen, and Jian-Kang Dong, “Color clustering using self-organizing maps”, Wavelet Analysis and Pattern Recognition(ICWARR), vol.3, pp. 986 - 989, 2007.
[42] Naoki Terahara, Yoshiro Oba, and Hiroomi Hikawa, “Color-space image compression with hardware Self-organizing map”, IIntelligent Signal Processing and Communications Systems(ISPACS), pp. 11 - 16, 2016.
[43] Microsoft Microsoft Developer Network, “MFC 桌面應用程式”, [Online] Available: https://msdn.microsoft.com/zh-tw/.
[44] OpenCV, “About Open Source Computer Vision Library”, [Online] Available: http://opencv.org/.
[45] C. Kampouris, S. Zafeiriou, A. Ghosh, and S. Malassiotis, “Fine-grained material classification using micro-geometry and reflectance”, Computer Vision(ECCV), pp.778 - 792, 2016.
[46] D. S. Guru, and H. N. Prakash, “Online Signature Verification and Recognition: An Approach Based on Symbolic Representation”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.31, no.6, pp. 1059 - 1073, 2009.
[47] Mark D. Fairchild, “Color Appearance Models”, 3rd Edition, The Wiley-IS&T Series in Imaging Science and Technology, ISBN. 978-1-119-96703-3, 2013.